What is digital health?
Digital health can be known as the epic quantum of technology generation of the recent leap (Samuel, 1959). It consists of linking the edges of care in order to ensure that the health information is disseminated resolutely. This linking is done electronically and it can help in improving the healthcare services as well as improve the delivery (Steinhubl, 2013). In this recent era the machine learning has become opposite with the digital health, it does not withstand the actual threat to machine learning (Samuel, 1959). This can be called for a rapid attention because there are numerous avalanches of algorithms that produce from numerous digital healths. Digital health as well as machine learning proves to be more acceptable. It has been a very important key word in the field of healthcare (Goodfellow & Yoshua, 2016). The particular system lacks adequate perception of using the computer systems in order to perform the tasks which actually need human intelligence. This utilization of the digital health is very spontaneous (Greenspan, Hayit & Bram, 2016). The data and algorithms received from the other devices that are related lead copious, intricacy and convoluted algorithms (Samuel, 1959). This vast change is instigated because of the increasing trend of using various internet devices, access to the internet, numerous devices that can be worn as well as smart phones that are generated by the applications and devices (Jordan, Michael & Tom 2015). As an effect, there are various ways to forward as well as improve the conduct, enterprise aggregate and advance of the data set as well as an algorithm (Qureshi, 2014). And in addition, how to interpret and utilise them to better fitness care transport that will favour, the purchaser (patient), health practitioners, and health stakeholders (Carlos, Ruth & Charles 2018). The modern-day innovation by means of some agency like Mayo-clinic, IBM Watson Health, Google (Deep Mind) towards evolving advanced digital fitness care gadget is considerable to these trends (Shen, Dinggang & Guorong 2017).
For better understanding, it is very important to the elementary and authentic formation of digital health (Qureshi, 2014). Digital health can be defined as the integration of health with various digital technologies (Chekroud, Adam & Ryan, 2016). It also helps the society to enhance the actual effectiveness of healthcare delivery and makes the manufacturing of medicines more precise and personalized (Bhavnani, et al., 2016). This discipline includes the usage of communication and information technologies in order to help to find out the health problems as well as challenges that patients face (Bhavnani, et al., 2016). The technologies are in the form of software or hardware and the services nd solutions like telemedicine, email, applications and mobile phones, text messages, remote or clinic monitoring sensors and wearable devices (Widmeret al., 2015). The main interest of the digital health of about improving the health systems that are inter related that would in return improve the usage of smart devices, computational techniques as well as communication media for aiding the healthcare professionals and patients to manage the health risks (Obermeyer, Ziad, & Ezekiel 2016). It also promotes good health and wellbeing.
Pillars of digital health technology
The unexpected and very fast acceptance level of the digital health is increases by the innovations brought about in the three pillars of technology of digital health (Erickson, Bradley, Panagiotis 2017). These pillars include medical sensing, computing and telecommunications. This has been shown in the figure below. The changes that have been brought about by the technologies of digital health especially the technologies that is new and associated with Web 2.0 innovations (Qureshi, 2014).
Most publications have been written from a generally uncritical preventive clinical or health promoting point of view and undertake a techno-utopian perspective, tending to laud the possibilities of these technologies besides acknowledging the social, ethical and political implications of their use (Cabitza, Federico, Raffaele 2017). From a necessary sociological perspective, however, an extra in-depth and nuanced analysis may be undertaken of how these technologies may additionally function to construct a number of forms of subjectivities and embodiments and participate in the configuring and replica of energy relations. Digital health is a multi-disciplinary domain which entails many stakeholders, including clinicians, researchers and scientists with an extensive range of expertise in healthcare, engineering, social sciences, public health, health economics and management (Lu, Chia-Feng & Fei-Ting 2018). The functionality of future digital fitness systems to translate and successfully radically change this lack of actionable facts to a meaningful one stays one of the key challenges in creating smarter more personalized and efficient digital Health shipping systems (Shen, Dinggang & Guorong 2017).
The fact that medical field uses numerous process implies that using human labor is safer than using computers for several purposes (Holzinger, Andreas & Igor Jurisica, 2014). Since around a decade the advancement in using electronic appliances for medical care has been advancing. The fact that should be considered in this case is that the data provided by technologies is not better than the previously used charts that have been replaced (Shen, Dinggang & Guorong 2017). If the technology aims in improving care in the future time, then the data provided to the healthcare professionals should be enhanced with help of the power of machine learning and analytics. The usage of these types of advances analytics, doctors can be provided with better data during the patient care (Shen, Dinggang & Guorong 2017). It would help the professionals in having easy access to vital signs like blood pressure. Clinicians need more information which would be useful for them in making better decisions regarding the treatments as well as diagnosis of the patients (Shen, Dinggang & Guorong 2017). It also helps them to estimate the treatments that can be undergone by the patients and the cost incurred in those treatments. The actual use of machine learning in the field of healthcare is its capacity to process large datasets which is not possible in the scope of human labour (Shen, Dinggang & Guorong 2017). After the data has been collected its analysis has been carried out and its results are converted into numerous clinical insights which help the physicians in proving care to patients with proper planning (Shen, Dinggang & Guorong 2017). This would ultimately lead in good outcomes along with fewer costs incurred in it. It also increases the satisfaction of patients. It has been said before that the tool of machine learning which proves to be best in the medicine field is the brain of the doctor. This sometimes leads in healthcare professionals’ judge the tools of machine learning as less wanted (Shen, Dinggang & Guorong 2017). Usage of machine learning also creates fear of unemployment within the professionals. In the same way numerous physicals bear the fear that machine learning is considered as the starting of a process which would make them obsolete but it should not be forgotten that physicians in the field of medicine can never be forgotten or replaced. Patients would require human touch and care along with a compassionate relation with the professionals who deliver care (Shen, Dinggang & Guorong 2017). Machine learning as well as any future technology would fail in proving the affection and care to the patients that are given by human physicians (Shen, Dinggang & Guorong 2017). They can just be used as tools that would help the professionals in improving the care provided to the patients. The main focus of professionals should be in the ways by which machine learning can be used in order to augment patient care (Shen, Dinggang & Guorong 2017). An example of this is suppose a professional is testing a patient for a dangerous disease called cancer, then the professional would require t he best quality of results obtained from the biopsy. Machine earning can be used in reviewing the slides of pathology and assist the physician with a specific diagnosis (Shen, Dinggang & Guorong 2017). If the results are obtained I very less amount of time with the help of machine learning then it would automatically contribute in providing early as well as better treatment to the patient. Machine learning in medicine field is also known to provide more accurate results compared to the ones provided by humans.
Benefits and challenges of using machine learning in healthcare
Machine learning can be defined as the field of computer science that uses statistical techniques in order to give the computer systems the capacity to learn. With the information, that is not being programmed (Koza, 1996).
Machine learning could be defined as a broad field within computer science, which makes use of techniques based on statistics. In the ancient times, “machine learning” was defined as the generation of artificial form of knowledge based on gaining several forms of experience (Shen, Dinggang & Guorong 2017). The first form of studies based on machine learning had been often been performed by games. There have been major forms of advancements in the recent times. In the recent progressing of technologies, it has been seen that the new form of information and communication based technologies have helped in majorly transforming the society and have thus affected all of the forms of life (Shen, Dinggang & Guorong 2017). The recent forms of technological advancements have led to a wide form of progress in various fields such as systems based on intelligent transportation. Various forms of technological advancements have also shown major form of progress in the varied fields of education, healthcare and agriculture (Qureshi, 2014). The rise of Machine Learning has made tremendous forms of impact within the society. They have thus made important forms of advancements that have led to the increase in the number of important forms of development of applications in the field of healthcare and medicine. The use of Machine Learning could be useful for making different forms of decisions based within clinical purposes (Signorini, 1999). In the recent times, there have been tremendous forms of advancements in the growth of the Machine Learning tools and techniques (Shen, Dinggang & Guorong 2017). It is regarded as the fast form of growing based field of technology. This form of technology has intersected within the fields of statistics and informatics. The technology has also tightly connected within the field of data science, discovery of knowledge areas and has also penetrated in the field of healthcare. With the growth of the technology in these fields they have also invited several forms of challenges that are also a major form of concern for the use of Machine Learning technologies (Shen, Dinggang & Guorong 2017).
The field of healthcare should not only consider machine learning as a concept for future, they should see it as a tool of real world which can be deployed in today’s world (Shen, Dinggang & Guorong 2017). The field should find some use cases where the capability of machine learning provides certain values from some technological applications like Stanford and Google (Shen, Dinggang & Guorong 2017). This would be a pathway that would lead in incorporating more analytics, predictive algorithms and machine learning into everyday’s practice of clinical research. Initially the goals of the physicians should meet their capabilities as well (Shen, Dinggang & Guorong 2017). Training an algorithm of machine learning in order to identify a patient suffering from skin cancer from a huge collection of images of skin cancer is something is understood by most of the people. Now this can be considered as a fact that if physicians were replaced by algorithms of machine learning, people would hesitate. Radiologists can never be obsolete but the future radiologists would supervise the reviews and readings that were initially read by a particular machine (Shen, Dinggang & Guorong 2017). Machine learning would be employed as a collaborative partner who would identify specific areas that should be focused, would illuminate noise and would help in focusing on the areas that are of high importance (Shen, Dinggang & Guorong 2017). Implementation of machine learning in healthcare would be questionable enough because people would find it difficult to reach the threshold that is needed in order to have faith in machine learning. Machines have various methods which help them in investigating as well as proving that the treatments suggested by them are effective as well as safe. This process is very long and consists of numerous trials and errors and the decisions are taken base on various evidences (Shen, Dinggang & Guorong 2017). These similar processes are used in order to ensure that the treatments provided are safe as well as have efficacy. The ethics involved in handling the part of what has been done to the machine is very important to know.
How machine learning can improve patient care
Machine Learning is regarded as the most precious form of new form of artificial intelligence technologies. This form of technology is based on providing different forms of ability to the computer systems. They have the ability for provide the computer systems for providing them with different forms of ability to perform different kinds of tasks in an efficient manner. The use of artificial intelligence provides immense forms of capabilities to the technology within various forms of programming tools and techniques (Shen, Dinggang & Guorong 2017). This form of technology makes use of data pattern analysis in order to learn new kinds of rules based on the collected data. These kinds of new rules within the Machine Learning technologies would be able to handle the technologies in a better form based on better modes of diagnose and predicting the possible form of outcomes. In the recent technological period, the use of Big Data and other forms of analysis could be used efficiently for making a good guess over the processing of data or the ways in which the technologies would need to be amended (Shen, Dinggang & Guorong 2017). The way of speaking, the words spoken, the choices that are made and the use of global positioning system would be able to helpful for analysing and thus be able to influence the behavioural systems. The data that is being generated would be able to take any form of action and thus provide different forms of recommendation (Shen, Dinggang & Guorong 2017). These would thus be able to meet the demands within the current market.
The use of machine learning makes use of different forms of sets of data. They also require huge forms of clinical variables for a particular program to detect such forms of nuances and thus detect patterns for deriving different forms of predictions (Shen, Dinggang & Guorong 2017). The machines based on processing these kinds of information focus on the gathered information and thus reach upto certain forms of conclusions after the data has been processed. They make use of neural network. These forms of networks would be able to learn different forms of rules based on the massive forms of data that are processed (Shen, Dinggang & Guorong 2017). The primary form of challenge in the output based on neural network learning is that they might not be able to show the exact form of outcomes that are required. This is one of the major forms of limitations based on medical form of artificial intelligence and the cases of machine learning (Shen, Dinggang & Guorong 2017). Different experts based within the domain of machine learning should be able think about the ways in which there should be improvements within the field of Machine Learning that might be able to mitigate the challenges and thus improve the technologies.
Conclusion
Burgeoning various applications of machine learning in medicine are glimmers of a good and shining future where the synchronicity of analysis, innovation and data is present in the everyday reality (Shen, Dinggang & Guorong 2017). Some applications of machine learning in medicine are as follows
Identification of disease: diagnosis of various alignments and identification of disease is a great research in machine learning in the field of medicine (Shen, Dinggang & Guorong 2017). As per a report that had been issued by the Pharmaceutical Research and manufacturers of America, almost 800 vaccines and medicines were in trial that were supposed to treat cancer (Shen, Dinggang & Guorong 2017). It is a fact that drug testing is exciting but it also faces numerous challenges regarding it success. Every vaccine or medicine that is in trial does not become successful. One example of medicine field utilizing machine learning is that the DeepMind Health of Google had announced numerous partnerships that were UK based (Shen, Dinggang & Guorong 2017). These partnerships included the ones with MoorFields Eye Hospital that is located in London where they have been developing the technology that would detect the macular degeneration in eyes cause due to aging. In the field of disease that are brain based such as depression, the Oxford P1vital Predicting Response to Depression Treatment project has been using various predictive analysis to help the professionals to diagnose as well as provide them treatment (Shen, Dinggang & Guorong 2017). Their goal is to produce an emotional test battery that would be commercially available to everyone. This battery would be used in clinical settings.
Behavioural modification and personalized treatment: personalized medicine can be defined as a treatment that is more effective on the basis of an individual’s health data that has been paired with the predictive analytics. This research is also a very recent one and is related to the better assessment of disease. The domain has been recently ruled by the supervised learning that allows various professionals in the field of medicine to select more amounts from the given limited sets of diagnoses. Within few years, the medicine field would use devices and micro biosensors along with various mobile apps which would have even more sophisticated remote monitoring and health measurement capabilities. It would also provide a different deluge of data which can be utilized in order to help the R&D function properly and maintain its efficiency.
Discovery or manufacturing of drugs: the utilization of machine learning in drug discovery in the early stages had the potential of numerous users starting from the screening of the drug to the prediction of its success rate on the basis of its biological factors. This includes the discovery of technologies of R&D like the sequencing of next generation. Precision medicine involves the identification of various mechanisms for the diseases that are multi factorial. It also suggests various alternative paths for curing those diseases. Most part of this particular research involves the learning that has not been supervised.
Clinical trial research: machine learning has numerous applications that are useful in helping the professionals to shape as well as direct their clinical trial research. Applying predictive analysis that is advanced in the field of identifying various candidates for the clinical trials would draw in a very wide range of information than recent, along with the social media as well as doctor visits.
Smart electronic health records: documentation classification like sorting the queries of patients through email and using various support vendor machines as well as optical character reorganization both are necessary technologies that are based on machine learning. These technologies help in advancing the digitization and collection of information regarding electronic health. The technology for handwriting recognition by MATLAB and the technology of Cloud Vision API by Google for optical character recognition are two examples of the innovations in this field. The machine learning group of MIT has been spread heading the development of the intelligent electronic health records of next generation. This would incorporate built-in ML/AI to help with various things such as clinical decisions, diagnostics as well as treatment sessions that can be personalized.
Prediction of epidemic outbreak: Machine learning has been used for predicting as well as monitoring epidemic outbreaks all over the world on the basis of data that has been collected from the satellites, historical information on internet, real time updates on social media and many more sources (Alanazi et al., 2017). Artificial neural networks and Support vendor machines had been used, an example of this is to predict the malaria outbreaks, taking in account data like average monthly rainfall, temperature, overall number of positive cases and many more other information points (Alanazi et al., 2017). Prediction of outbreak severity has been pressing in the third world countries that actually lack proper infrastructure, access of the treatments and educational avenues in medical field (Alanazi et al., 2017). ProMED-mail is a very well known reporting system that is internet based and helps in monitoring emerging diseases as well as providing reports for outbreak in real time.
The Machine Learning technologies are mainly categorised in two different aspects:
- Supervised Machine Learning–This is a form of machine learning technique that is primarily deducted from a labelled form of training. This form of collection of raw data would be primarily be based on the collection of data samples (Alanazi et al., 2017). They would also be composed of a set of training examples. Each of the examples would be thus supervised in a particular form of training of the dataset (Alanazi et al., 2017). They would comprise of a pair of objectives based on input, the input vector and a preferable form of output vector. In a supervised form of machine learning technology, the algorithm would be able to analyse the training of data. They would thus be able to produce an inferred form of function, which is known as a classifier (Alanazi et al., 2017). The kind of deducted function would be able to analyse the output value of any form of suitable input methods. This kind of approach would mean that the learning algorithm would be able to generalize the training data based on the previous unobservable situations. This kind of tasks would be able to be known within the concept of animal and human psychology.
- Unsupervised Machine Learning–This form of Machine Learning technique would be able to relate different kinds of situations that would attempt to find different kinds of hidden structures within unmarked data (Shen, Dinggang & Guorong 2017). The reward signal is a form of crucial factor that would be able to distinguish between the unsupervised and supervised form of machine learning. The unsupervised form of machine learning technology would be able to relate the different kinds of learning based technologies (Shen, Dinggang & Guorong 2017). These kinds of algorithms would be able to present the models of neural network that would also include self-organizing map and an adaptive resonance theory.
There are various forms of the use of machine learning technologies (Alanazi et al., 2017). They are very much useful for the use in health informatics. In these kinds of situations, most of the problems would mainly involve the dealing of the technologies with a vast form of uncertainty.
Surgical Predictions–The algorithms based on machine learning are mainly used for defining accurate measures for people who are at a greater form of risk based on complications at the post surgery (Thottakkara, 2016). The wide form of expansion of the different kinds of models could be deeply focused based on nursing based on postanesthesia. They would be able to cause different forms of radical kinds of changes in many of the practices within the industry (Alanazi et al., 2017). Machine Learning has thus become a tool based on the magnification and complementing of the processes of nursing.
Just-in-time Information – The applications based on Machine Learning could also be helpful for lowering down of such kinds of situations based on lowering the stakes within the managerial levels (Alanazi et al., 2017). The reduction of the burden of documentation and the introduction of just-in-time information would be helpful for the improvement of efficiency and thus satisfying the different kinds of electronic systems (Alanazi et al., 2017). The use of Machine Learningtools would be helpful for processing the text and voice commands into different forms of recommended documents. They would also be able to provide different kinds of technical related tools (Alanazi et al., 2017). The robust nature of Machine Learning tools and techniques would allow the real-time target based on supporting of nurses. They would thus be able to navigate the big portions of Big Data.
The use of Machine Learning has been a major area of research for the purpose of predicting a disease in their initial stages. There are many doctors who would be in the massive need if predicting the accuracy of the concerned disease (Alanazi et al., 2017). The timing is the most crucial factor that would be able to influence the vast forms of decisions based on the treatment of the disease.
According to a research by Alanaziet al., 2017, it has been seen that the different forms of conducted surveys based on the current nature of machine learning. They thus make use of a predictive model within the field of healthcare (Alanazi et al., 2017). There was also a conducted survey based on the severity of TBI that had made the usage of neural networks (Guleret al., 2008). The outcome of the survey was a dataset was based on 32 patients who were possessing different forms of demographic based characteristics (Alanazi et al., 2017). The primary form of the accuracy of the predictive model was based on 91%. Another form of analysis that was conducted byFlemminget al., 2001revealed the fact that a DT analysis that would be combined by a CT scan could be able to predict the GOS of death, vegetative state and dependence on discharge. The study had analysed 81 patients who were possessing lobar haemorrhage. They had thus presented the study within 48 hours based on the early detection of neurologic signs.
There are different kinds of monitoring based systems, which are mainly been assisted by machine learning. They make use of the technology of Internet of Things (IoT) for the continuous form of evaluation of monitoring the health of the people. The use of Machine Learning have thus been helpful for analysing the different forms of correlation with the data collected from the particular environment (Alanazi et al., 2017). These data would be generally being collected based on the IoT technologies for the purpose of monitoring and thus be able to facilitate the physical form of well-being of the people who would suffer from dementia.
Multi-task Learning (MTL) is one of the future form of challenges within the field of healthcare. The primary objective is to advance the activities based on the learning of a problem in a multiple manner (Alanazi et al., 2017). They would also be related to each other with the help of shared form of parameters or with the help of a shared form of representation. The underlying principle of MTL is based on bias learning. They would be dependent on probable approximately correct learning (PAC learning) (Valiant, 1984). The detection of such kind of bias would be one of the hardest forms of problem in any of the ML based tasks (Alanazi et al., 2017). The pre-existing methods of bias would be generally based on the bigger sets of data. The existing methods would generally require the form of input based on a human expert. However the use of these kinds of methods would have the basic forms of limitations based on the levels of accuracy and the factors of reliability on the knowledge of the concerned expert. Baxter (2000) had thus introduced a kind of model based on bias learning. This form of methods based on learning would be able to build itself based on the factor of bias learning (Alanazi et al., 2017).
The factor of transfer learning is also a major form of problem within Machine Learning. It could be defined as the phenomenon based on catastrophic forms of forgetting (Alanazi et al., 2017). There are several forms of ethical based issues based within machine learning.
Conclusion
Based on the discussion from the above report, it could thus be concluded that the emerging field of machine learning could serve as a major purpose in the field of healthcare. The healthcare workers would be able to view the effect of the technology in a better manner. This technology offers great promises for the future works of research in the particular field of study. The impact of Machine Learning tools and technologies would be able to remove the different forms of biasness, exhaustion and the other kinds of limitations based on massive kinds of computations.
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